Data Science 简明教程
Data Science - Resources
本文列出了 2023 年你可以参加以提升你的技能并获得最佳的数据科学家工作之一的最佳数据科学项目和课程。你应该参加其中一项数据科学家的在线课程和认证，以踏上精通数据科学的正确道路。
This article lists the best programs and courses in data science that you can take to improve your skills and get one of the best data scientist jobs in 2023. You should take one of these online courses and certifications for data scientists to get started on the right path to mastering data science.
Top Data Science Courses
在这一部分，我们将讨论互联网上一些流行的数据科学课程。
In this section we will discuss some the popular courses for data science that are available on the internet.
编制 2023 年顶级数据科学课程列表时考虑了多种因素/方面，包括：
A variety of factors/aspects were considered when producing the list of top data science courses for 2023, including −
@s0 − 编译列表时考虑了教学大纲的广度，以及它满足不同经验水平的有效程度。
Curriculum Covered − The list is compiled with the breadth of the syllabus in mind, as well as how effectively it has been tailored to fit varied levels of experience.
Course Features and Outcomes − 我们还讨论了课程成果和其他方面，比如查询解决、实践项目等，这将帮助学生获得适销对路的技能。
Course Features and Outcomes − We have also discussed the course outcomes and other aspects, such as Query resolve, hands-on projects, and so on, that will help students obtain marketable skills.
Course Length − 我们已经计算了每门课程的时长。
Course Length − We have calculated the length of each course.
Skills Required − 我们已经讨论了申请者参加课程必须具备的技能要求。
Skills Required − We have addressed the required skills that applicants must have in order to participate in the course.
Course Fees − 根据每门课程的特点和价格对课程进行评级，确保你物有所值。
Course Fees − Each course is graded based on its features and prices to ensure that you get the most value for your money.
Course Highlights
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Covers all areas of data science, beginning with the fundamentals of programming (binary, loops, number systems, etc.) and on through intermediate programming subjects (arrays, OOPs, sorting, recursion, etc.) and ML Engineering (NLP, Reinforcement Learning, TensorFlow, Keras, etc.).
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Lifetime access.
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30-Days Money Back Guarantee.
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After completion certificate.
Course Duration: 94 小时。
Course Duration: 94 hours.
查看课程详情 here
Check the course details here
Course Highlights
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This course will enable you to build a Data Science foundation, whether you have basic Python skills or not. The code-along and well planned-out exercises will make you comfortable with the Python syntax right from the outset. At the end of this short course, you’ll be proficient in the fundamentals of Python programming for Data Science and Data Analysis.
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In this truly step-by-step course, every new tutorial video is built on what you have already learned. The aim is to move you one extra step forward at a time, and then, you are assigned a small task that is solved right at the beginning of the next video. That is, you start by understanding the theoretical part of a new concept first. Then, you master this concept by implementing everything practically using Python.
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Become a Python developer and Data Scientist by enrolling in this course. Even if you are a novice in Python and data science, you will find this illustrative course informative, practical, and helpful. And if you aren’t new to Python and data science, you’ll still find the hands-on projects in this course immensely helpful.
Course Duration: 14 小时
Course Duration: 14 hour
查看课程详情 here.
Check course details here.
Course Description
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The course demonstrates the importance and advantages of R language as a start, then it presents topics on R data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames and lists. Besides, it includes topics on operators, conditionals, loops, functions, and packages. It also covers regular expressions, getting and cleaning data, plotting, and data manipulation using the dplyr package.
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Lifetime access.
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30-Days Money Back Guarantee.
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After completion certificate.
Course Duration: 6 小时
Course Duration: 6 hours
查看课程详情 here.
Check the course details here.
在本课程中，你将学习 −
In this course you will learn about −
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Life Cycle of a Data Science Project.
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Python libraries like Pandas and Numpy used extensively in Data Science.
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Matplotlib and Seaborn for Data Visualization.
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Data Preprocessing steps like Feature Encoding, Feature Scaling etc…
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Machine Learning Fundamentals and different algorithms
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Cloud Computing for Machine Learning
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Deep Learning
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5 projects like Diabetes Prediction, Stock Price Prediction etc…
Course Duration: 7 小时
Course Duration: 7 hours
查看课程详情 here.
Check the course details here.
Course Description
此 Pandas 课程全面介绍了此功能强大的工具，用于实施数据分析、数据清理、数据转换、不同数据格式、文本操作、正则表达式、数据 I/O、数据统计、数据可视化、时间序列等。
This Course of Pandas offers a complete view of this powerful tool for implementing data analysis, data cleaning, data transformation, different data formats, text manipulation, regular expressions, data I/O, data statistics, data visualization, time series and more.
本课程是一门实践课程，例子众多，因为学习最简单的方法就是通过实践！然后，我们将把我们学到所有知识整合到一个 Capstone 项目，利用著名的 IMDB 数据集来开发初步分析、清理、过滤、转换和可视化数据。
This course is a practical course with many examples because the easiest way to learn is by practicing! then we’ll integrate all the knowledge we have learned in a Capstone Project developing a preliminary analysis, cleaning, filtering, transforming, and visualizing data using the famous IMDB dataset.
Course Duration: 6 小时
Course Duration: 6 hours
查看课程详情 here.
Check the course details here.
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This course is meant for beginners and intermediates who wants to expert on Python programming concepts and Data Science libraries for analysis, machine Learning models etc.
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They can be students, professionals, Data Scientist, Business Analyst, Data Engineer, Machine Learning Engineer, Project Manager, Leads, business reports etc.
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The course have been divided into 6 parts - Chapters, Quizzes, Classroom Hands-on Exercises, Homework Hands-on Exercises, Case Studies and Projects.
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Practice and Hands-on concepts through Classroom, Homework Assignments, Case Studies and Projects
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This Course is ideal for anyone who is starting their Data Science Journey and building ML models and Analytics in future.
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This course covers all the important Python Fundamentals and Data Science Concepts requires to succeed in Academics and Corporate Industry.
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Opportunity to Apply Data Science Concepts in 3 Real World Case Studies and 2 Real World Projects.
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The 3 Case Studies are on Loan Risk Analysis, Churn Prediction and Customer Segmentation.
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The 2 Projects are on Titanic Dataset and NYC Taxi Trip Duration.
Course Duration: 8.5 小时
Course Duration: 8.5 hours
查看课程详情 here.
Check the course details here.
Course Description
学生们将获得关于统计学基础的知识
Students will gain knowledge about the basics of statistics
他们将对不同类型数据有清晰的理解，包括对理解数据分析非常重要的示例
They will have a clear understanding of different types of data with examples which is very important to understand data analysis
学生们将能分析、解释和解读数据
Students will be able to analyze, explain and interpret the data
他们将通过学习皮尔森相关系数、散点图以及变量之间的线性回归分析来了解关系和依赖性，并且将能够了解如何进行预测
They will understand the relationship and dependency by learning Pearson’s correlation coefficient, scatter diagram, and linear regression analysis between the variables and will be able to know to make the prediction
学生们将了解不同的数据分析方法，例如集中趋势的测量（平均值、中值、众数）、离散度的测量（方差、标准差、变异系数）、如何计算四分位数、偏度和箱线图
Students will understand the different methods of data analysis such as a measure of central tendency (mean, median, mode), a measure of dispersion (variance, standard deviation, coefficient of variation), how to calculate quartiles, skewness, and box plot
他们将能够在学习偏度和箱线图后清晰地理解数据形状，这是数据分析中的一个重要部分
They will have a clear understanding of the shape of data after learning skewness and box plot, which is an important part of data analysis
学生们将对概率有基本的理解，懂得如何利用最简单的示例来解释和理解贝叶斯定理
Students will have a basic understanding of probability and how to explain and understand Bayes theorem with the simplest example
Course Duration: 7 小时
Course Duration: 7 hours
查看课程详情 here.
Check the course details here.
Top Data Science ebooks
在此部分中，我们将讨论可在网上找到的一些关于数据科学的流行电子书。
In this section we will discuss some the popular ebooks for data science that are available on the internet.
Beginners Course on Data Science
在这本电子书中，你将找到关于开始学习数据科学以及熟练掌握其方法和工具所需了解的所有内容。了解数据科学以及它如何帮助预测在当今快节奏的世界中至关重要。本书的目的是提供对数据科学及其方法的高级概述。数据科学起源于统计学。然而，在此领域取得成功需要在编程、商业和统计学方面具备专业知识。最佳的学习方式是深入熟悉每个学科。
In this book, you’ll find everything you need to know to get started with data science and become proficient with its methods and tools. Understanding data science and how it aids prediction is crucial in today’s fast-paced world. The purpose of this book is to provide a high-level overview of data science and its methodology.Data Science has its origins in statistics. However, expertise in programming, business, and statistics is necessary for success in this arena. The best way to learn is to familiarize yourself with each subject at length.
在数据集中发现趋势和见解是一项古老的艺术。古埃及人使用人口普查信息来更好地征税。尼罗河洪水预测也是使用数据分析做出的。在数据集中找到模式或令人兴奋的见解片段需要回顾之前的数据。该公司将能够利用这些信息做出更好的选择。数据科学家的需求不再隐蔽；如果你喜欢分析数字信息，那么这就是你的领域。数据科学是一个不断发展的领域，如果你决定接受这方面的教育，那么你应该抓住机会，在它出现时立即开始从事这项工作。
Finding trends and insights within a dataset is an age-old art. The ancient Egyptians used census information to better levy taxes. Nile flood predictions were also made using data analysis. Finding a pattern or exciting nugget of information in a dataset requires looking back at the data that came before it. The company will be able to use this information to make better choices.The need for data scientists is no longer hidden; if you enjoy analyzing numerical information, this is your field. Data Science is a growing field, and if you decide to pursue an education in it, you should jump at the chance to work in it as soon as it presents itself.
查看电子书 here.
Check the ebook here.
Building Data Science Solutions With Anaconda
在本书中，您将学习如何将 Anaconda 用作轻松按钮，让您全面了解 conda 等工具的功能，其中包括如何指定新渠道以提取所需的任何软件包，以及发现可以使用的新的开源工具。您还将清晰地了解如何评估要训练的模型，以及识别它们何时因漂移而变得不可用。最后，您将了解可用于解释模型工作原理的强大而简单的技术。
In this book, you’ll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works.
在本课程结束时，您将自信地使用 conda 和 Anaconda Navigator 来管理依赖关系，并全面了解端到端数据科学工作流。
By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
查看电子书 here.
Check the ebook here.
Practical Data Science With Python
本书首先概述了基本的 Python 技能，然后介绍基础数据科学技术，接下来是执行这些技术所需的 Python 代码的详尽说明。您将通过学习示例来理解代码。代码已被分解为小块（一次几行或一个函数），以便进行深入讨论。
The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You’ll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.
随着您的进步，您将学习如何执行数据分析，同时探索关键数据科学 Python 包的功能，包括 pandas、SciPy 和 scikit-learn。最后，本书涵盖了数据科学中的道德和隐私问题，并提出了提高数据科学技能的资源，以及随时了解新的数据科学发展的途径。
As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.
在本课程结束时，您应该能够轻松地使用 Python 进行基本的数据科学项目，并具备在任何数据源上执行数据科学流程的技能。
By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
查看电子书 here.
Check the ebook here.
Cleaning Data for Effective Data Science
本书深入探讨了数据导入、异常检测、值填补和特征工程所需工具和技术的实际应用。它还提供每一章末尾的长篇练习，以练习所获得的技能。
The book dives into the practical application of tools and techniques needed for data ingestion, anomaly detection, value imputation, and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired.
您将首先查看 JSON、CSV、SQL RDBMS、HDF5、NoSQL 数据库、图像格式文件和二进制序列化数据结构等数据格式的数据导入。此外，本书提供了大量示例数据集和数据文件，可供下载和独立探索。
You will begin by looking at data ingestion of data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, and binary serialized data structures. Further, the book provides numerous example data sets and data files, which are available for download and independent exploration.
从格式开始，您将填补缺失值、检测不可靠的数据和统计异常，并生成成功数据分析和可视化目标所需的合成特征。
Moving on from formats, you will impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features that are necessary for successful data analysis and visualization goals.
在本课程结束时，您将对执行现实世界数据科学和机器学习任务所需的数据清理流程有一个坚定的理解。
By the end of this book, you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks.
查看电子书 here.
Check the ebook here.
Essentials of Data Science And Analytics
本书结合了数据科学和分析的关键概念，帮助您对这些领域有实际的了解。本书的四个不同的部分被分成章节，解释了数据科学的核心。鉴于对数据科学的兴趣激增，这本书是及时且内容丰富的。
This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.
查看电子书 here.
Check the ebook here.